Automated network design and traffic steering

ABSTRACT

Initiation of a network slice event is disclosed. The network slice event can be initiated in response to, and according to a determined a network slice event instruction. The network slice event can result in modification of network slices of a network. The modification of the network slices can correspond to a change in the performance of the network. The modification of the network slices can comprise adding a new slice, removing an existing slice, adapting an existing slice, etc. Artificial intelligence, machine learning, etc., can be employed to provide an inference related to determining the network slice event instruction. The slice event can be implemented via a network controller, for example an ONAP component, based on the network slice event instruction.

TECHNICAL FIELD

The disclosed subject matter relates to modifying a network slice and,more particularly, to automating design of a slice, initiation of aslice event, and subsequent employment of resulting network slice(s),based on a network analytic.

BACKGROUND

Next-generation mobility networks, including 5G cellular networks andsystems, are anticipated to enable disruptive digital transformation inthe society that will enable people, machines, businesses andgovernments with unprecedented capabilities to communicate and shareinformation effectively. Beyond the cutting-edge radio accesstechnologies, 5G aims to integrate cross-domain networks so that serviceproviders can offer network-on-demand as a service. With the advances in5G, new mobility services, convergence of fixed and rich mobile servicesacross several industry verticals and new services-revenue-businessmodels can be enabled. The demands on 5G can be high in terms ofhandling a variety of use cases associated with mobile-to-mobile and the‘internet of things’ (M2M/IoT), augmented/virtual reality (AR/VR),telehealth, targeted mobile advertising, connected cars etc. These newservices can require a wide range of aggregate bit rates, low latencies,vehicular speeds, device types and device capabilities, devicedensities, etc., to provide consistent end user quality for a givenservice in heterogeneous environment.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is an illustration of an example system that can facilitateinitiation of a network slice event, in accordance with aspects of thesubject disclosure.

FIG. 2 is an illustration of an example system that can facilitatemodification of network slices based on coordinated analysis of networkanalytic data for a plurality of network components, in accordance withaspects of the subject disclosure.

FIG. 3 is an illustration of an example system that can enable deployinga new slice via an open network automation platform (ONAP) componentbased on analysis of network analytic data, in accordance with aspectsof the subject disclosure.

FIG. 4 illustrates an example system that can facilitate modification ofnetwork slices based on new slice design information in response todetermining an inference based on coordination of network analytic data,in accordance with aspects of the subject disclosure.

FIG. 5 illustrates an example system providing for provisioning of a newnetwork slice based a determined inference and/or received user input,in accordance with aspects of the subject disclosure.

FIG. 6 is an illustration of an example method enabling initiation of aslice event for modifying a group of network slices, in accordance withaspects of the subject disclosure.

FIG. 7 illustrates an example method facilitating determining a sliceevent based on a traffic profile inference determined from receivedtraffic analytic data and employing the resulting network slices, inaccordance with aspects of the subject disclosure.

FIG. 8 illustrates an example method enabling initiating a slice eventvia a network automation platform device based on coordinated analysisof network analytic data from at least two network analytic datasources, in accordance with aspects of the subject disclosure.

FIG. 9 depicts an example schematic block diagram of a computingenvironment with which the disclosed subject matter can interact.

FIG. 10 illustrates an example block diagram of a computing systemoperable to execute the disclosed systems and methods in accordance withan embodiment.

DETAILED DESCRIPTION

The subject disclosure is now described with reference to the drawings,wherein like reference numerals are used to refer to like elementsthroughout. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the subject disclosure. It may be evident, however,that the subject disclosure may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to facilitate describing the subjectdisclosure.

As mentioned, modern networks, e.g., 5G, 6G, etc., can be highlyresource intensive in terms of handling mobile-to-mobile (M2M),‘internet of things’ (IoT), augmented/virtual reality (AR/VR),telehealth, targeted mobile advertising, connected cars, and otherapplications/services/technologies. As such, modern networks can requirea wide range of aggregate bit rates, targeted latencies, support fordevice types and device dependent capabilities, scalability for variousdevice densities, etc., to provide consistent end user quality for agiven use in a heterogeneous networking environment. Given that acentralized network architecture model with a single set of standardmobility network functions can be extremely complex and expensive todeploy in a manner that can be able to meet the demanding performancerequirements for a wide variety of mobility services, network slicingconcepts can enable use of standardized network elements and functionsin a manner that can be dynamically re-configurable within a networkoperator architecture to be able to create and deliver a given mobilityservice. Logically slicing a core network into multiple virtualnetworks, hereinafter referred to as core network (CN) slice(s), canenable designation of and/or optimization of the CN slice to meetdynamically changing demands on the CN. Moreover, in addition to corenetwork resources, slicing can be equally applicable to a radio accessnetwork (RAN) as well. Given the scarce physical radio resources of aRAN, and their allocations and utilizations in space, frequency and timedomains, it can be possible to define end-to-end network slicing,wherein one or more CN slice(s) is intelligently paired with one of moreRAN slice(s) to adaptively define an end-to-end network-on-demand forservices employing differing application(s) and/or service(s), businessagreement(s), etc. Hereinafter, a ‘network slice’ typically refers to acore network slice, a radio access network slice, another type of slice,or combinations thereof, unless otherwise expressly or inherentlyindicating otherwise.

In an aspect a RAN can comprise devices enabling an end device locatedat an edge of a network, e.g., a mobile device, user equipment (UE), IoTsensor, tablet computer, PC, etc., to connect to a network of a wirelessnetwork provider. In some embodiments, a RAN can comprise a wiredconnection, a wireless connection, or both. As an example, a RAN cancomprise a NodeB or eNodeB enabling a user equipment (UE) to connect viaa wireless link to a network of a wireless network provider. In anotherexample, a RAN can comprise a femtocell, picocell, etc., that canprovide a wireless link to the example network. In an aspect, a RANcomponent can provide one or more RAN technologies, for example, arouter can provide a wired link, a WiFi link, e.g., an IEEE 802.xxconnection, etc., a Bluetooth® link, a cellular link, etc. These links,portions thereof, combinations thereof, etc., can act as one or more RANslices.

Generally speaking, a slice can be a virtualization of a physicalnetwork that enables independent architecture, partitioning, andorganization of computing resources in each slice. This can facilitateflexibility that is typically not readily available in a monolithicembodiment of a physical network. A physical RAN can be sliced in tovirtual RAN slices such that the one or more virtual RAN slices can eachbe adapted according to corresponding characteristics, e.g., adapted toperform a specific type of communication or service better than ageneric channel of the monolithic physical RAN. Similarly a CN slice canalso be a virtualization of a physical CN resource. Typically a slice,e.g., either a RAN or CN slice, can be considered self-contained withregard to operation, traffic flow, performance, etc., can have its ownvirtualized architecture and features, and can be individuallyprovisioned in a network. The virtualization of physical networkresources via slicing can simplify creation, management, and operationof slices, typically tailored to a type of functionality, environment,service, hardware, etc., to enable efficient consumption of networkresources of the physical network. As examples, a first slice can have afirst bandwidth and a second slice can have a different secondbandwidth; a first slice can have a different latency than a secondslice; a first slice can employ different virtual functions, e.g., VNFs,than a second slice; etc. As disclosed herein, selection of a RAN sliceand/or a CN slice can provide benefit to a network by efficientlyemploying the resources of the end-to-end network, such as by pairing anarrow spectral RAN slice with a CN slice that supports IoT devices viaVNFs frequently employed by an IoT device, which can be more efficientthan pairing a wide spectral RAN slice with the same CN slice, wherethis can waste the extra spectrum allocated via the wide spectral RANslice. Other more nuanced examples are readily appreciated andconsidered within the scope of the presently disclosed subject mattereven where not explicitly recited.

Pairing or binding can adaptively employ a RAN slice(s), a CN slice(s),or a combination of slice(s) to provide desired or indicated features,performance, cost, efficiency, etc. Moreover, in contrast to randompairing between RAN and Core slices, e.g., based on pre-defined staticallocations, intelligent pairing can allocate a resource(s) in real timeor near real time, and in a manner that can reflect business goals. Lackof an intelligent pairing function for RAN and core slices, andreal-time sharing of such information to a network slice coordinator,can impact network functionality as well as targeted mobilityapplications and services. End-to-end network slicing can be referred toas ‘network slicing’ and a network slice can comprise either or both aRAN slice(s) and a CN slice(s), as distinct from CN slicing that doesnot consider a RAN slice, and as distinct from RAN slicing that does notconsider a CN slice.

Network slicing can transform a monolithic mobility networkingarchitecture that has traditionally been used to service smartphones inthe current wireless network provider industry. With the proliferationof new wireless technologies and next generation mobile devices, theconnectivity and communication models can be expected to rapidly evolveand drive the adoption of new services which were not possible before.Moreover, as network functions transform from physical to virtualdomain, e.g., in a cloud centric environment, etc., this transformationcan open up innovative opportunities to be able to design fullyprogrammable mobile networks, for example, network that can deliver a‘micro-service architecture’, etc. Programmable or adaptive networktechnology concepts can be applied to core networks and can extended toradio access networks, to provide radio resources and create a robustnetwork slicing concept that can work in a coordinated manner.

Under network slicing technologies, within a single frequency band for aRAN device, each carrier can be split into one or more slice(s) that canbe selectable in terms of their utilization in space, time, frequencydomain, etc. Each such slice, and combinations of such slices, can beemployed within a single carrier-band and/or across multiple carriersfor different sets of services, e.g., based on device requirements in areal-time or near real-time manner. Thus, dynamic spectrum management,for example as disclosed herein, can enable spectral allocation viaconfigurable network slices, e.g., pairing of a RAN slice and CN slice,adaptation of a RAN/CN slice pair, etc., in one or more region(s).Spectrum slicing granularity available within a RAN can enableallocation of a fine RAN slice to provide service toapplications/services of one or more device across one or more groups ofdevices, with similar or identical characteristics, for example, narrowband IoT devices that can operate in a 200 kHz channel for infrequentand short data transmission can employ a RAN slice that is narrow andtemporally multiplexed to serve the one or more IOT devices, which canalso be performed across one or more geographic regions. The example IOTdevices can be devices such as, but not limited to, sensors, utilitymeters that can wake up to report their readings and then return to anextended sleep mode, parking meters that report upon use then return toa sleep mode, etc.

Moreover, the example RAN slice can be re-allocated, for example, as astandalone resource, etc., combined with other radio slices perappropriate rules for aggregation, etc., to satisfy changing serviceconditions/requirements, e.g., where the RAN slice can be subsequentlyused to provide service to other devices including mobile broadbandsmartphones, more spectrum demanding classes of IoT devices, etc. TheRAN slice allocation can adapt in real-time, or near real-time, and canmaintain a record of historical RAN slice allocation(s), pairing(s),etc., to facilitate future use by the example less demanding IoT devicesas they are deployed, though subject to prompt adaptation based oncurrent spectral/performance demands. As such, analysis of informationpertaining to the device/service using the spectrum, in addition toanalysis of the RAN slice and/or CN slice, can therefore enableintelligent use of historical information to facilitate allocation of anetwork slice, which can then be adapted based on thedemands/performance in the present use of the network slice. In anembodiment, the analysis can be performed based on the historicalinformation and current use prior to allocation of the network slice,e.g., RAN slice-CN slice pair. In an embodiment, the historicalinformation can be employed to select an initial network slice that canthen subsequently be adapted based on the current use. Adaptation of anetwork slice can comprise adaptation of the RAN slice, adaptation ofthe CN slice, or both. Moreover, the adaptation can be used to updatestored historical data. Adaptation of the RAN slice can include changesto the time, frequency, space, etc., of the RAN slice, merging RANslices, divesting RAN slices, ranking RAN slices, ordering RAN slices,shifting a RAN slice in frequency, time, space, etc., coordinated usewith another RAN slice, etc. Adaptation of the CN slice can merge CNslices, divest CN slices, rank CN slices, order CN slices, coordinateuse with another CN slice, add/remove functionality to a CN slice, e.g.,adding/removing one or more virtual network function (VNF) to a CNslice, etc. Adapting a RAN slice or CN slice can be performed in anautomated manner, e.g., in a software deployed network (SDN), vianetwork function virtualization (NFV), etc. As an example, intelligentselection of a network pair can result in offloading non-criticaltraffic from a RAN slice, CN slice, RAN slice group, CN slice group,etc., to a different RAN or CN slice based on, for example, the priorityof the services in a given location, a subscriber agreement parameter,historical use by the device requesting access to a service via thenetwork pair, planned/unplanned maintenance of a RAN device or CNdevice, changes in use of RAN resources, availability of alternate VNFs,etc.

In an aspect, a component facilitating communication between an UE andanother device via a network can provide network analytics data (NAD).NAD can represent performance of a portion of a communication frameworkfrom the UE to the other device, e.g., RAN device performance, corenetwork device performance, etc. In an aspect, the performance can beconsidered a network state. Moreover, where there are different networkstates, each state can represent a corresponding performance or group ofperformances related to the performance of portions of the network forthe given network state. The term ‘state’ can generally be interchangedwith the term ‘performance’ or ‘characteristic’ without departing fromthe scope of the presently disclosed subject matter. Whereas NAD can begenerated by a variety of devices in the communication path,coordination of the NAD from the corresponding network analyticssource(s) can provide an opportunity to improve management of networkslice(s), e.g., via initiation of a slice event that can modify thenetwork slice(s), such as adding a new network slice, removing anexisting network slice, altering a performance of a network slice,modifying trigger conditions for deploying additional an network slice,etc. Furthermore, artificial intelligence and/or machine learning(AI/ML) can be employed to facilitate automation of slice event(s) andutilization of resulting network slices. Further, use of networkautomation platform components can enable design/provisioning/deploymentof slices in accord with inferences formed from coordinated analysis ofNAD from one or more network analytics source. The inference can beconstrained by rules, training sets, etc., that can be provided toenable the AI/ML to better meet user expectations for a given networkand given conditions of the network.

To the accomplishment of the foregoing and related ends, the disclosedsubject matter, then, comprises one or more of the features hereinaftermore fully described. The following description and the annexed drawingsset forth in detail certain illustrative aspects of the subject matter.However, these aspects are indicative of but a few of the various waysin which the principles of the subject matter can be employed. Otheraspects, advantages, and novel features of the disclosed subject matterwill become apparent from the following detailed description whenconsidered in conjunction with the provided drawings.

FIG. 1 is an illustration of a system 100, which can facilitateinitiation of a network slice event, in accordance with aspects of thesubject disclosure. System 100 can comprise master analytics engine(MAE) 110. MAE 110 can receive network analytics data (NAD) 130 from anetwork analytics source(s) 120. Network analytics source(s) 120 canprovide analytics data from various portions of a network, e.g., from aradio access network (RAN) portion of the network, from a core networkportion of the network, from a transport layer portion of the network,from an applications/services portion of the network, etc. As anexample, a car manufacturer can launch self-driving trucks that cantransport goods along a transportation corridor. The car manufacturercan work with a network provider to dedicate a first network slice forself-driving truck traffic in order to provide edge connectivity for theself-driving trucks that achieves low latency and high throughput thatwould be beneficial to the self-driving aspect of the trucks. As theself-driving trucks become increasingly more popular, additionaltransportation corridors can start to see increasing use. Data can becollected from various data sources that illustrate where someadjustment can be made in the network slices that include the firstnetwork slice. These data sources can correspond to different portionsof the network, e.g., a RAN analytics engine can provide first NADrelated to mobility patterns evolution for the self-driving trucks touse more than the transportation corridor for which the first slice wasdesigned; a transport analytics engine: can indicate that some truckscan be experiencing higher latency on the routes outside of the originaltransportation corridor; a core analytics engine can indicate that an‘edge core’ is becoming increasingly inefficient in serving self-drivingtruck traffic, e.g., due to trucks using more distant transportationcorridors, increased network traffic density as the number ofself-driving trucks in a region increases, etc.; an applicationanalytics engine can illustrate traffic patterns for self-driving trucksare increasingly augmenting, e.g., application alarm(s) can indicatethat requests are timing out due to higher latency, putting the trucktraffic at risk, etc. MAE 110 can coordinate NAD 130 to enablemodification of network slices.

MAE 110 can access or receive a rule via rule store 140. A rule can beemployed to respond to the coordinated analysis of NAD 130 by MAE 110.As an example, determining that a rule related to an amount of trafficon a slice is satisfied can result in MAE 110 generating an instructionrelated to provisioning an additional slice to carry some of thetraffic, carry new traffic, etc. As another example, determining that arule related to quality of service (QoS) is satisfied can result in MAE110 generating an instruction related to altering a bandwidth of anetwork slice to alter the QoS level of the network slice. Numerousother examples are to be appreciated by one of skill in the art and allsuch examples are within the scope of the present disclosure despite notbeing enumerated for the sake of clarity and brevity.

MAE 110 can enable access to slice event initiation information (SEII)190. SEII 190 can result in modification of network slices. In someembodiments, SEII 190 can be generated by MAE 110 based on NAD 130and/or a rule from rule store 140. Continuing the earlier self-drivingtruck example, a master analytics engine, e.g., MAE 110, can compileinformation received from the other analytics sources, e.g., NAD 130from network analytics source(s) 120. MAE 110 can identify, for example,that new trucks have been deployed in large numbers and are these newtrucks can now be employing additional transportation corridors otherthan the originally planned for transportation corridor. A new slice canbe deployed in response to MAE 110 facilitating access to slice eventinitiation information, e.g., SEII 190, etc. The new slice can employnetwork resources closer to the new transportation corridor routes usedby the additional self-driving trucks that have been deployed. As stillfurther self-driving trucks are deployed, they can be placed on the newslice. Further, as the use of the network slices continues to evolve,MAE 110 can continue to respond to changes by modifying network slicesbased on coordinated analysis of NAD from the various parts of thenetwork.

FIG. 2 is an illustration of a system 200, which can enable modificationof network slices based on coordinated analysis of network analytic datafor a plurality of network components, in accordance with aspects of thesubject disclosure. System 200 can comprise MAE 210, which can receiveNAD, e.g., NAD 230, 232, 234, etc., from network analytics source(s),e.g., RAN 202, core network component(s) 204, etc. MAE 210 can analyzethe received NAD(s) and enable access to SEII 290. SEII 290 can resultin modification of network slices.

SEII 190 can be received by network controller 250. In an embodiment,network controller 250 can comprise a network automation platformcomponent such as an open network automation platform (ONAP) component.As such, network controller 250 can enable core network component(s) 204to receive controller information 260 that can facilitate a slice eventinitiation via instructions 262 that can result in modification ofnetwork slices, e.g., first slice 205, second slice 206, third slice207, etc., for example, creation of new slice 208, deletion of a slice(not illustrated), adaptation of a slice (not illustrated), etc.

In an aspect, the modification of the network slices via MAE 210 canresult in communications between UE 201 and device 202 to employ thesame or a different slice, e.g., the communication path 2011 can passbetween UE 201 and device 202 via RAN 202 and core network component(s)204, e.g., via one or more of slice(s) 205-208. In an embodiment, RAN202 can communicate slice selection information 2021 to slice selectioncomponent 203, and slice selection component 203 can indicate, viaselected slice information 2031, which CN slice to employ. Moreover,although not illustrated for clarity and brevity, a RAN slice can alsobe modified via receiving controller information 260. RAN 202 canreceive controller information 260 directly from network controller 250,indirectly via core network component 204, etc. In some embodiments,network controller 250 and slice selection component 203 can be embodiedin the same component or device, or can even be the samecomponent/device, but are illustrated as separate components in FIG. 2for clarity and brevity.

FIG. 3 is an illustration of a system 300, which can facilitatedeploying a new slice via an ONAP component based on analysis of networkanalytic data, in accordance with aspects of the subject disclosure.System 300 can comprise MAE 310 that can receive NAD 330 from a networkanalytics source(s) 320. Network analytics source(s) 320 can provideanalytics data from various portions of a network. Data can be collectedfrom various data sources that illustrate where some adjustment can bemade in the network slices. These data sources can correspond todifferent portions of the network. MAE 310 can analyze NAD 330 to enablemodification of network slices.

MAE 310 can enable access to SEII 390. SEII 390 can enable modificationof network slices. In some embodiments, SEII 390 can be generated by MAE310. SEII 390 can be based on NAD 330, an inference determined via AI/MLcomponent 370, a rule from rule store 340, etc. Modification of networkslices can comprise removing a network slice, modifying a network slice,adding a network slice, etc., e.g., new slice 308 can be deployed, basedon controller information 360, in response to MAE 310 facilitatingaccess by ONAP component 350 to SEII 390.

System 300 can employ AI/ML technology to improve automation of slicemodification. In an aspect, this can aid in reducing, or eveneliminating, the need for human responses to changes in networkperformance. It is not uncommon for conventional slice technologies tosend a performance alert to a user such that the user can indicatemodification of slices. In an aspect, rules can be set in MAE 310 thancan allow for automation of slice modification techniques. However, theaddition of AI/ML technology can allow inferences to be formed ordetermined, for example, based on training data, monitoring of actualresults of slice modifications, etc., in order to ‘learn’ and makeimproved further slice modification decisions. AI/ML component 370 canaccess or receive a rule via rule store 340. In some embodiments a rulecan comprise training data to teach AI/ML component 370. Additionally, arule can be employed to direct a response based on NAD 330, e.g., viaAI/ML component 370, MAE 310, etc. As an example, a hospital in a denseurban environment can be using a large number of medical supplies andemploy instrumentation equipment enabled by internet-of-things (IoT)type wireless communications. As supplies are consumed, as new IoT-typeequipment arrives, etc., these items can be inventoried, tracked via awireless network, etc., as an example, utilization of the IoT-typeequipment in that hospital can be tracked. A network slice canfacilitate the these, and other aspects, of the hospital operations.

An appropriate network slice can be created and adapted dynamically inreal time, or near real time, to facilitate the hospital operations. Thenetwork slice can be based on historical data trends of the networkinfrastructure used by the hospital; based on new requirements thatcould be entered as rules, as user input, etc., a training AI/MLtraining set, etc. The slice being deployed can include an automationframework that can use real-time analytics and monitoring to adapt useof network resources via a network controller, e.g., ONAP, etc., based,for example, on triggers from an AI/ML engine, etc., to better meettraffic demands and targeted service needs of the hospital. In thisexample scenario, portions of the communications framework, e.g., RAN,transport, core, applications, virtualized functions, etc., can feedanalytics data to a master analytics engine, e.g., MAE 110, 210, 310,etc. The MAE can enable design of a slice, e.g., having specificcapabilities and associated metrics derived from policy driven rules,etc. The MAE can track, for example, an amount of resources utilized inthe slice to perform a telehealth service, unused resources that couldbe shared with a different slice, etc. As such, design, creation, and/oractivation of slices on a common infrastructure in a hospitalinformation technology (IT) data center can be enhanced via the use ofanalysis from the MAE which automates a slice event by, for example,providing triggers to an ONAP engine to provide a template for a newslice design based on evolving service requirements. Moreover, having aclosed-loop system, as described in this example, can enable trackinginventory, refill of required supplies, efficient monitoring/utilizationof medical equipment via cellular IoT network, etc., and can enable thehospital to maintain a competitive edge and deliver superior patientcare services. The hospital can therefore leverage the example automatedslice event technology in their IT infrastructure to alter utilizationof equipment, resources, etc., for example, in a more efficient manner(both locally and/or remotely) for in-house, as well as remote,diagnosis of patients. Numerous other examples are to be appreciated byone of skill in the art and all such examples are within the scope ofthe present disclosure despite not being enumerated for the sake ofclarity and brevity.

FIG. 4 is an illustration of a system 400, which can enable modificationof network slices based on new slice design information in response todetermining an inference based on coordination of network analytic data,in accordance with aspects of the subject disclosure. System 400 cancomprise MAE 410 that can receive NAD, e.g., NAD 130, 230-234, 330,etc., from a network analytics source(s), e.g., RAN analytics source420, data transport analytics source 422, core analytics source 424,applications analytics source 426, etc. The network analytics source(s),e.g., 420-426, etc., can provide analytics data for various portions ofa network, e.g., correspondingly for RAN component(s) 421, transportcomponent(s) 423, core component(s) 425, applications component(s) 427,etc., based on data from those portions of the network. In an aspect,the various portions of the network, e.g., 421-427, etc., can behardware, hardware and software, virtualized network functions, softwaredeployed network components, etc. In an aspect, the various portions ofthe network, e.g., 421-427, etc., can exist in a network functionvirtualization/software deployed network (NFV/SDN) layer of the network.Data can be communicated from the various portions of the network, e.g.,421-427, etc., to the network analytics source(s), e.g., 420-426, etc.,which network analytics source(s) can provide access to analytics datafrom the various portions of the network to MAE 410, which can thenperform a coordinated analysis to of the analytics as part ofdetermining SEII 490. Analytics data, as will be readily appreciated cancomprise processed data, e.g., analytic data, related to the variousportions of the network. Moreover, Analytics data can comprise raw datafrom various components of the various portions of the network, eitheralone or in combination with processed data. In an aspect, the networkanalytics source(s), e.g., 420-426, etc., and/or MAE 410 can becomprised in an analytics layer of the network.

System 400 can employ AI/ML technology, e. g via AI/ML component 470,etc., to improve automation of slice modification. AI/ML component 470can enable determining an inference, e.g., an inference related to aslice event, initiation of a slice event, triggering a slice event,design of a slice, evaluation of a slice for given network analytics orgiven analytics for a portion of a network, ranking of a slice relativeto another slice based on a characteristic of the slice, a criterion,etc., ordering of slices, filtering of slices, modification of slices,etc. The inference, in an embodiment, can be based on training of AI/MLcomponent 470, e.g., via a training data set, etc. As will be readilyappreciated by those of skill in the art, the use of AI/ML technologycan readily improve the automation of slice event initiation and, insome embodiments, can enable removing or reducing human interaction ininitiating a network slice event. As is previously noted, conventionalslice technologies can interact with a user, such that the user can besubstantially involved in modification of slices via initiation of aslice event. However, the addition of AI/ML technology can allowinferences to be formed or determined, for example, based on trainingdata, monitoring of actual results of slice modifications, etc., inorder to ‘learn’ and make slice modification decisions, often in lieu ofa human user/operator. As such, AI/ML component 470 can communicate withMAE 410 to provide inference determining ability to sliced eventinitiation techniques. This inference can therefore be represented inSEII 490.

MAE 410 can enable access to SEII 490. SEII 490 can enable modificationof network slices. In some embodiments, SEII 490 can be generated by MAE410. SEII 490 can be based on NAD, e.g., from network analyticssource(s), e.g., 420-426, etc., an inference determined via AI/MLcomponent 470, a rule, e.g., from rule store 140, 340, etc., orcombinations thereof. Modification of network slices can compriseremoving a network slice, modifying a network slice, adding a networkslice, etc. Where, for example, a new slice is to be deployed as part ofa slice event, FIG. 4 illustrates access to new slice design information460. New slice design information 460, can be accessed, for example,from ONAP component 450. ONAP component 450 can, for example, enableaccess to new slice design information 460 in response to receiving SEII490 via MAE 410. As an example, a hypothetical chat application canlaunch a new augmented reality (AR) feature. Historically, traffic forthis chat application can sustain high latency and relatively smallthroughput but can be considered ‘bursty’ in nature. Accordingly, therehas historically been no special treatment for this chat applicationtraffic and it has been relegated to being part of a general purposenetwork slice. However, launch of the AR feature can be very successfuland the chat app with AR features might go viral. Accordingly, within acouple of days, millions of subscribers can be demanding low latency andhigh throughput to operate the AR feature of the chat application. Theevolution of the network demands by the chat application can be capturedin analytics, e.g., data regarding the latency of the service, type oftraffic, location and mobility of users, throughput, etc., for variousportions of the network and a MAE, e.g., MAE 410, etc., can determinethat the evolving traffic pattern should be served via a new slice withmore appropriate characteristics than the existing general networkslice. The MAE can generate SEII, e.g., SEII 490, etc., that can causeinitiation of a new dedicated slice, e.g., via an ONAP component, suchas ONAP component 450, etc. The MAE can further inform, direct,instruct, etc., that UEs be directed to the new slice. In an aspect,directing the UEs to the new slice can comprise directing new UEs to thenew slice, existing UEs to the new slice, or combinations thereof, e.g.,existing chat app instances can be re-directed to the new slice, newchat app instances can be directed to employ the new slice, or acombination thereof.

FIG. 5 is an illustration of a system 500, which can enable provisioningof a new network slice based on a determined inference and/or receiveduser input, in accordance with aspects of the subject disclosure. System500 can comprise MAE 510 that can receive NAD 530 from network analyticssource(s) 520. Network analytics source(s) 520 can provide analyticsdata for various portions of a network, e.g., a RAN portion, a transportportion, core portion, an applications/services portion, etc., based ondata from those portions of the network, e.g., from network datasource(s) 521. In an aspect, network data source(s) 521 can be networkhardware, network hardware and network software in combination,virtualized network functions, software deployed network components, orother real-world portions of a communications network. MAE 510 canperform an analysis of received NAD 530 as part of determining SEII 590.Analytics data, as will be readily appreciated can comprise processeddata, e.g., analytic data, related to the various portions of thenetwork. Moreover, Analytics data can comprise raw data from variouscomponents of the various portions of the network, either alone or incombination with processed data.

System 500 can comprise AI/ML component 570 that can generate aninference that can facilitate automation of network slice modification.The inference can be related, for example, to a slice event, initiationof a slice event, triggering a slice event, design of a slice,evaluation of a slice for given network analytics or given analytics fora portion of a network, ranking of a slice relative to another slicebased on a characteristic of the slice, a criterion, etc., ordering ofslices, filtering of slices, modification of slices, etc. In anembodiment, AI/ML component 570 can be trained based on training data, atraining data set, etc. AI/ML technology can readily improve automationof slice event initiation, for example, by facilitating the removal ofsome or all human interaction in initiating a network slice event. As ispreviously noted, conventional slice technologies can interact with auser, such that the user can be substantially involved in modificationof slices via initiation of a slice event. However, the addition ofAI/ML technology can allow inferences to be formed or determined, forexample, based on training data, monitoring of actual results of slicemodifications, etc., in order to ‘learn’ and make slice modificationdecisions, often in lieu of a human user/operator. As such, AI/MLcomponent 570 can communicate with MAE 510 to provide inferencedetermining ability to sliced event initiation techniques. Thisinference can therefore be represented in SEII 590.

In an aspect, AI/ML component 570 can leverage real time, or near realtime, data feeds from MAE 510 that can track a networking environment,e.g., both cross-functional and cross-layer networking characteristics,along with the trending of various service specific behaviors supportedat a slice level. AI/ML component 570 can operate with a networkcontroller, such as an ONAP component that can use an open standardsframework (APIs/Procedure Calls) to drive new slice configurations andnew policies into a core network function that may not have beenpreviously available on an existing slice. AI/ML component 570 cantrigger dynamic instantiation of a network slice that can provide adifferent end user experience by taking into account various datainsights, e.g., available spectrum, resource utilization, userpopulation, network functions needed in a targeted slice, duration etc.Moreover, AI/ML component 570 can continue to extract real time, or nearreal time, insight from operational characteristics of a newly deployedslice, e.g., via MAE 510. This can, in some embodiments, act as aclosed-loop feedback system that can adapt service levels to contractedlevels, etc. Embodiments of system 500 can dynamically adjustdomain-specific design metrics by altering individual domain functionsto alter operational network efficiency.

Moreover, AI/ML technology, e.g., via AI/ML component 570, canfacilitate reducing the burden placed on operators by, for example,reducing a number of choices/proposed solutions for a network sliceevent, such that the operator can more readily chose from these‘pre-selected’ slice events proposed by MAE 510 based on inference fromAI/ML component 570. In embodiments of system 500, an operator, e.g., auser, etc., can provide input as user input 530 via user interface (UI)component 512. Accordingly, AI/ML component 570 can interact with MAE510 to provide a group of slice events that can be presented to anoperator. This group of slice events can already be determined to bepreferable to other slice events based on an inference from AI/MLcomponent 570. As such, embodiments of system 500 can improve existingslice deployment technologies by providing for ‘pre-selection’ ofpreferred slice event aspects. Moreover, differences between the sliceevents of the group presented to the operator can be highlighted tofurther aid the operator in selecting, e.g., via UI component 512, aslice event that can then be embodied in SEII 590 via MAE 510.

MAE 510 can enable access to SEII 590. SEII 590 can enable modificationof network slices. In some embodiments, SEII 590 can be generated by MAE510. SEII 590 can be based on NAD 530, an inference determined via AI/MLcomponent 570, a rule from rule store 540, user input 530, etc.Modification of network slices can comprise removing a network slice,modifying a network slice, adding a network slice, etc. Where, forexample, a new slice is to be deployed as part of a slice event, FIG. 5illustrates access to new slice design information 560. New slice designinformation 560, can be accessed, for example, by network data source(s)521 from ONAP component 550. ONAP component 550 can, for example, enableaccess to new slice design information 560 in response to receiving SEII590 via MAE 510. Resulting new slice 508 can then be instantiated based,for example on new slice design information 560.

In view of the example system(s) described above, example method(s) thatcan be implemented in accordance with the disclosed subject matter canbe better appreciated with reference to flowcharts in FIG. 6-FIG. 8. Forpurposes of simplicity of explanation, example methods disclosed hereinare presented and described as a series of acts; however, it is to beunderstood and appreciated that the claimed subject matter is notlimited by the order of acts, as some acts may occur in different ordersand/or concurrently with other acts from that shown and describedherein. For example, one or more example methods disclosed herein couldalternatively be represented as a series of interrelated states orevents, such as in a state diagram. Moreover, interaction diagram(s) mayrepresent methods in accordance with the disclosed subject matter whendisparate entities enact disparate portions of the methods. Furthermore,not all illustrated acts may be required to implement a describedexample method in accordance with the subject specification. Furtheryet, two or more of the disclosed example methods can be implemented incombination with each other, to accomplish one or more aspects hereindescribed. It should be further appreciated that the example methodsdisclosed throughout the subject specification are capable of beingstored on an article of manufacture (e.g., a computer-readable medium)to allow transporting and transferring such methods to computers forexecution, and thus implementation, by a processor or for storage in amemory.

FIG. 6 is an illustration of an example method 600, which facilitatesinitiating a slice event causing modification of a group of networkslices, in accordance with aspects of the subject disclosure. At 610,method 600 can comprise receiving traffic analytic data. Trafficanalytic data can be related to a characteristic of a portion of acommunication network, such a RAN portion of a wireless communicationnetwork, a core network portion thereof, a data transport portionthereof, an application/service portion thereof, etc. In an aspect, thetraffic analytic data can reflect a performance characteristic that canbe altered, for example by moving traffic onto network slice of amodified group of network slices. As an example, network analytic datacan indicate that a RAN network slice performance is approaching athreshold limit due to an amount of traffic employing the RAN networkslice. Accordingly, deploying a new RAN network slice that is designedto accommodate more traffic can allow use of the new RAN network sliceto alleviate or avoid performance degradation by continuing to use theprior RAN network slice with increased traffic. Numerous other examplesare readily appreciated and are to be considered within the scope of thepresent disclosure despite not being enumerated for the sake of clarityand brevity. The portions of a network can comprise, among otherportions, a RAN portion, a transport portion, core portion, anapplications/services portion, etc. Traffic analytic data, e.g., NAD130, 230-234, 330, etc., can be coordinated via a master analyticsengine, e.g., MAE 110, 210, 310, 410, 510, etc.

At 620, method 600 can comprise, generating a traffic profile. Thetraffic profile can be generated by MAE, e.g., MAE 110, 210, 310, 410,510, etc., based on the received traffic analytic data. In an aspect,the traffic profile can correspond to a performance of a network thatcan be leveraged to determine a slice event that can alter theperformance of the network. In some embodiments, the traffic profile canbe based on a rule corresponding to the coordination of the trafficanalytic data, as an example, a rule can weight some traffic analyticdata differently than other traffic analytic data to influence selectionof a slice design that can be implemented via initiation of a sliceevent to alter the performance of a network via modification of networkslices of the network. In some embodiments, the traffic profile can bebased on inferences related to the traffic analytic data, e.g., aninference generated by an AI/ML component, such as, AI/ML component370-570, etc.

Method 600, at 630, can comprise determining a slice event instructionbased on the traffic profile. Modification of a group of slices cancomprise removing a slice, adding a slice, altering a slice, etc. Assuch, the determining the slice event instruction can be related toadding, removing, or altering a network slice. Accordingly, a sliceevent can alter a group of network slices which can result in acorresponding alteration in network performance. The determining a sliceevent instruction can be comprised in determining a group of slice eventinstructions that can correspond to interactions with a networkcontroller, such as an ONAP component, etc., that can enable causing acorresponding change in the network slices when the instruction, orgroup of instructions, are performed.

At 640, method 600 can comprise initiating a slice event. At this pointmethod 600 can end. The initiating the slice event can be based on theslice event instruction determined at 630. As a result of the initiatingthe slice event, existing network slices can be modified, e.g., a newslice can be added to the network slices, an existing slice of thenetwork slices can be altered or deleted, etc. The modification of thenetwork slices can result in an altered network performance. It is notedthat the slice event instruction from 630 can cause a particularmodification of the network slices. As an example, the slice eventinstruction can generate a new slice having a determined characteristic,design, performance, etc. In this example, more than one slice event canbe possible based on the traffic profile from 620, e.g., there can bemore than one possible slice that can alter the network performance uponimplementation and, accordingly, a preferred slice can be selected aspart of determining the slice event instruction at 630 to result ininitiating a preferred slice event at 640 that is at least intended toachieve a determined characteristic in network performance as a resultof the slice event modifying the network slices.

In an aspect, method 600 facilities initiating a determined slice event,e.g., a slice event that is preferred, a slice event that is rankeddifferently than other possible slice events, a slice event that meets aslice event criterion, a slice event that passes one or more slice eventfilters, a slice event that meets a slice event criterion, a slice eventthat is determined to satisfy a slice event rule, a slice event that isdetermined to be in accord with a slice event inference, etc. Moreover,method 600 enables automating a slice event via use of a traffic profilethat can be considerate of traffic analytic data from one or moreportions of a network. A rule can be applied against the traffic profileto indicate a network slice design characteristic that is preferred,such that automation via method 600 can, for example, order, rank, sort,filter, etc., possible network slice designs to satisfy the ruleallowing for determining a slice event instruction that can result in anetwork performance modification that is preferred over other possiblenetwork performance modification resulting from other possible sliceevents. Additionally, use of AI/ML techniques can further enableautomation of initiating a slice event based on an inference generatedbased on training the AI/ML. This can reduce or eliminate the use ofhuman operator instantiation of slices. Automation of the slicedeployment in a network can be a considerable improvement in networkmanagement and design in that it can be performed at speed substantiallybetter than would be expected with human operators, larger data sets canbe considered allowing for expected improved slice design, there can bereduction/elimination of operator training, there can be lower costs,new rules/preferences can be pushed out much more quickly than would beexperienced with human operator re-training, etc.

FIG. 7 illustrates example method 700 facilitating determining a sliceevent based on a traffic profile inference determined from receivedtraffic analytic data and employing the resulting network slices, inaccordance with aspects of the subject disclosure. Method 700, at 710,can comprise determining a traffic profile. The traffic profile can bedetermined in response to receiving traffic analytic data. The trafficprofile can be determined by a MAE, e.g., MAE 110, 210, 310, 410, 510,etc., based on the received traffic analytic data. Traffic analytic datacan be related to performance of at least a portion of a communicationnetwork. In an aspect, the performance can be altered. Traffic analyticdata, e.g., NAD 130, 230-234, 330, etc., can be coordinated via a masteranalytics engine, e.g., MAE 110, 210, 310, 410, 510, etc.

At 720, method 700 can comprise initiating a slice event. The initiatingthe slice event can be in response to determining a slice eventinstruction. The slice event can result in, cause, trigger, etc.,modification of a group of network slices, e.g., removing a slice,adding a slice, altering a slice, etc., that can result in a modifiedgroup of network slices. The determining the slice event instruction canbe based on the traffic pattern profile inference, e.g., from 710.Accordingly, the determining the slice event instruction can correspondto the adding, removing, or altering of a network slice, e.g., of thegroup of network slices, and result in the modified group of networkslices. In an aspect the modified group of network slices can performdifferently, e.g., have different performance, differentcharacteristics, different connections, etc., than the group of networkslices and can result in a corresponding modification to overall networkperformance.

At 730, method 700 can comprise receiving an indication that the sliceevent has occurred and, in response, can comprise facilitatingprovisioning of a user equipment to employ a slice of the modified groupof network slices. At this point method 700 can end. In embodiments ofthe disclosed subject matter, UEs can be steered onto slices of themodified group of network slices, such as from the group of networkslices. As an example, where a new network slice is added, a UE using anexisting slice can be directed to attach via the newly added networkslice. As another example, where a new network slice is added, a UE thatis newly attaching to the network can be directed to attach via thenewly added network slice. Numerous other examples are readilyappreciated and are to be considered within the scope of the presentlydisclosed subject matter despite, for the sake of clarity and brevity,not being further described.

In an aspect, method 700 facilities initiating a determined slice eventand directing network traffic onto the modified group of network slicesflowing from the initiating of the determined slice event. Method 700facilitates automating initiating a slice event. Additionally, use ofAI/ML techniques can further enable automation of initiating a sliceevent based on the traffic profile inference. The inference can be basedon training of the AI/ML. Human operator instantiation and/ormodification of slices can reduced or eliminated where the MAE and/orAI/ML component(s) can perform instating a slice event in a manner thatwill typically be fast, based on more accurate information, based onlarger sets of information, based on more comprehensive rules/goals,etc. The presently disclosed automation of slice event initiation for anetwork can be a substantial improvement over other modern techniques.

FIG. 8 illustrates example method 800 enabling initiating a slice eventvia a network automation platform device based on coordinated analysisof network analytic data from at least two network analytic datasources, in accordance with aspects of the subject disclosure. Method800, at 810, comprises receiving first traffic analytic data. The firsttraffic analytic data can correspond to a characteristic of a corenetwork portion of a wireless communications network, e.g., wirelessnetwork core-network analytics. Analytic data can be determined by anetwork component, e.g., network analytics source(s) 120, 320, 520,etc., core network component(s) 204, core analytics source 424, or othercomponents. The analytic data for the core network portion of thewireless network, e.g., core-network analytics, etc., can be received asfirst traffic analytic data.

At 820, system 800 comprises receiving second traffic analytic data thatcan correspond to a performance characteristic of another portion of thewireless communications network, e.g., wireless network core-networkanalytics, wherein the other portion is a different portion than thecore network portion. The other analytic data can be determined by acorresponding network component, e.g., network analytics source(s) 120,320, 520, etc., RAN 202, RAN analytics source 420, transport analyticssource 422, applications analytics source 426, or other components. Thesecond traffic analytic data can therefore correspond to performance ofportions of the wireless network, other than the core networkportion(s), that facilitate communication between a UE and the wirelessnetwork, e.g., as part of communicating with another device via acommunications pathway comprising at least a portion of the wirelessnetwork.

At 830, method 800 can comprise determining a slice event instructionthat corresponds to a traffic profile. The traffic profile can bedetermined from the first traffic analytic data and the second trafficanalytic data. In embodiments, additional traffic analytic data can alsobe employed in determining the traffic profile. The slice eventinstruction can, in an embodiment, direct a slice event upon initiationof the slice event. In an embodiment, the slice event instruction canitself direct initiation of the slice event. In an embodiment, the sliceinstruction can also cause initiating of the slice event and thepropagation of the slice event. Other embodiments are readilyappreciated, all of which are within the scope of the presently disclosesubject matter but are not further recited for clarity and brevity. Inan aspect, the slice event instruction can be an instruction for asingle action, a compound action, multiple actions, a complex action(s),a dependent action(s), a responsive action(s), etc.

At 840, method 800 can comprise initiating a slice event. At this point,method 800 can end. The slice event can be initiated via a networkautomation platform device. As an example an ONAP device ore componentcan be directed to perform actions corresponding to the slice event,e.g., the slice event can be initiated via the ONAP device or component.The initiating the slice event can be in response to the determining aslice event instruction at 830. The slice event can result in, cause,trigger, etc., modification of a group of network slices, e.g., removinga slice, adding a slice, altering a slice, etc., that can result in amodified group of network slices. In an embodiment of method 800, theinitiating the slice event can be in response to receiving the sliceevent instruction, e.g., the slice event instruction can cause the sliceevent to begin. In another embodiment of method 800, the initiating theslice event can result from the slice event instruction itself, e.g.,the slice event instruction being communicated can be the initiation ofthe slice event itself.

The slice event can cause modification of a group of network slices,e.g., adding, removing, or altering, and can result in a modified groupof network slices. In an aspect the modified group of network slices canperform differently than the group of network slices before themodification resulting from the slice event.

FIG. 9 is a schematic block diagram of a computing environment 900 withwhich the disclosed subject matter can interact. The system 900comprises one or more remote component(s) 910. The remote component(s)910 can be hardware and/or software (e.g., threads, processes, computingdevices). In some embodiments, remote component(s) 910 can comprisenetwork analytics source(s) 120, 320, 520, etc., RAN 202, core networkcomponent(s) 204, AI/ML component 370, 470, 570 etc., RAN analyticssource 420, transport analytics source 422, applications analyticssource 426, UI component 512, or other component(s) or device(s) thatare located remotely from MAE 110-510.

The system 900 also comprises one or more local component(s) 920. Thelocal component(s) 920 can be hardware and/or software (e.g., threads,processes, computing devices). In some embodiments, local component(s)920 can comprise MAE 110-510, etc., AI/ML component 370, 470, 570 etc.,or other component(s) or device(s) that are located local to MAE 110-510

One possible communication between a remote component(s) 910 and a localcomponent(s) 920 can be in the form of a data packet adapted to betransmitted between two or more computer processes. Another possiblecommunication between a remote component(s) 910 and a local component(s)920 can be in the form of circuit-switched data adapted to betransmitted between two or more computer processes in radio time slots.The system 900 comprises a communication framework 940 that can beemployed to facilitate communications between the remote component(s)910 and the local component(s) 920, and can comprise an air interface,e.g., Uu interface of a UMTS network, via a long-term evolution (LTE)network, etc. Remote component(s) 910 can be operably connected to oneor more remote data store(s) 950, such as a hard drive, solid statedrive, SIM card, device memory, etc., that can be employed to storeinformation on the remote component(s) 910 side of communicationframework 940. Similarly, local component(s) 920 can be operablyconnected to one or more local data store(s) 930, that can be employedto store information on the local component(s) 920 side of communicationframework 940. As examples, potential slice event instructions can bestored at MAE 110-510, e.g., on local data storage device(s) 930, etc.,to facilitate determining SEII 190-590, etc., rules can be stored onrule store 140, 340, 540, etc., e.g., on a remote data storage device(s)950, etc.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 10, and the following discussion, are intended toprovide a brief, general description of a suitable environment in whichthe various aspects of the disclosed subject matter can be implemented.While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer and/or computers, those skilled in the art will recognize thatthe disclosed subject matter also can be implemented in combination withother program modules. Generally, program modules comprise routines,programs, components, data structures, etc. that performs particulartasks and/or implement particular abstract data types.

In the subject specification, terms such as “store,” “storage,” “datastore,” data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It is noted that thememory components described herein can be either volatile memory ornonvolatile memory, or can comprise both volatile and nonvolatilememory, by way of illustration, and not limitation, volatile memory 1020(see below), non-volatile memory 1022 (see below), disk storage 1024(see below), and memory storage 1046 (see below). Further, nonvolatilememory can be included in read only memory, programmable read onlymemory, electrically programmable read only memory, electricallyerasable read only memory, or flash memory. Volatile memory can compriserandom access memory, which acts as external cache memory. By way ofillustration and not limitation, random access memory is available inmany forms such as synchronous random access memory, dynamic randomaccess memory, synchronous dynamic random access memory, double datarate synchronous dynamic random access memory, enhanced synchronousdynamic random access memory, SynchLink dynamic random access memory,and direct Rambus random access memory. Additionally, the disclosedmemory components of systems or methods herein are intended to comprise,without being limited to comprising, these and any other suitable typesof memory.

Moreover, it is noted that the disclosed subject matter can be practicedwith other computer system configurations, comprising single-processoror multiprocessor computer systems, mini-computing devices, mainframecomputers, as well as personal computers, hand-held computing devices(e.g., personal digital assistant, phone, watch, tablet computers,netbook computers, . . . ), microprocessor-based or programmableconsumer or industrial electronics, and the like. The illustratedaspects can also be practiced in distributed computing environmentswhere tasks are performed by remote processing devices that are linkedthrough a communications network; however, some if not all aspects ofthe subject disclosure can be practiced on stand-alone computers. In adistributed computing environment, program modules can be located inboth local and remote memory storage devices.

FIG. 10 illustrates a block diagram of a computing system 1000 operableto execute the disclosed systems and methods in accordance with anembodiment. Computer 1012, which can be, for example, comprised in MAE110-510, etc., network analytics source(s) 120, 320, 520, etc., RAN 202,core network component(s) 204, AI/ML component 370, 470, 570 etc., RANanalytics source 420, transport analytics source 422, applicationsanalytics source 426, UI component 512, etc., network controller 250,ONAP component 350, 450, etc., or nearly any other device, can comprisea processing unit 1014, a system memory 1016, and a system bus 1018.System bus 1018 couples system components comprising, but not limitedto, system memory 1016 to processing unit 1014. Processing unit 1014 canbe any of various available processors. Dual microprocessors and othermultiprocessor architectures also can be employed as processing unit1014.

System bus 1018 can be any of several types of bus structure(s)comprising a memory bus or a memory controller, a peripheral bus or anexternal bus, and/or a local bus using any variety of available busarchitectures comprising, but not limited to, industrial standardarchitecture, micro-channel architecture, extended industrial standardarchitecture, intelligent drive electronics, video electronics standardsassociation local bus, peripheral component interconnect, card bus,universal serial bus, advanced graphics port, personal computer memorycard international association bus, Firewire (Institute of Electricaland Electronics Engineers 1194), and small computer systems interface.

System memory 1016 can comprise volatile memory 1020 and nonvolatilememory 1022. A basic input/output system, containing routines totransfer information between elements within computer 1012, such asduring start-up, can be stored in nonvolatile memory 1022. By way ofillustration, and not limitation, nonvolatile memory 1022 can compriseread only memory, programmable read only memory, electricallyprogrammable read only memory, electrically erasable read only memory,or flash memory. Volatile memory 1020 comprises read only memory, whichacts as external cache memory. By way of illustration and notlimitation, read only memory is available in many forms such assynchronous random access memory, dynamic read only memory, synchronousdynamic read only memory, double data rate synchronous dynamic read onlymemory, enhanced synchronous dynamic read only memory, SynchLink dynamicread only memory, Rambus direct read only memory, direct Rambus dynamicread only memory, and Rambus dynamic read only memory.

Computer 1012 can also comprise removable/non-removable,volatile/non-volatile computer storage media. FIG. 10 illustrates, forexample, disk storage 1024. Disk storage 1024 comprises, but is notlimited to, devices like a magnetic disk drive, floppy disk drive, tapedrive, flash memory card, or memory stick. In addition, disk storage1024 can comprise storage media separately or in combination with otherstorage media comprising, but not limited to, an optical disk drive suchas a compact disk read only memory device, compact disk recordabledrive, compact disk rewritable drive or a digital versatile disk readonly memory. To facilitate connection of the disk storage devices 1024to system bus 1018, a removable or non-removable interface is typicallyused, such as interface 1026.

Computing devices typically comprise a variety of media, which cancomprise computer-readable storage media or communications media, whichtwo terms are used herein differently from one another as follows.

Computer-readable storage media can be any available storage media thatcan be accessed by the computer and comprises both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structureddata, or unstructured data. Computer-readable storage media cancomprise, but are not limited to, read only memory, programmable readonly memory, electrically programmable read only memory, electricallyerasable read only memory, flash memory or other memory technology,compact disk read only memory, digital versatile disk or other opticaldisk storage, magnetic cassettes, magnetic tape, magnetic disk storageor other magnetic storage devices, or other tangible media which can beused to store desired information. In this regard, the term “tangible”herein as may be applied to storage, memory or computer-readable media,is to be understood to exclude only propagating intangible signals perse as a modifier and does not relinquish coverage of all standardstorage, memory or computer-readable media that are not only propagatingintangible signals per se. In an aspect, tangible media can comprisenon-transitory media wherein the term “non-transitory” herein as may beapplied to storage, memory or computer-readable media, is to beunderstood to exclude only propagating transitory signals per se as amodifier and does not relinquish coverage of all standard storage,memory or computer-readable media that are not only propagatingtransitory signals per se. Computer-readable storage media can beaccessed by one or more local or remote computing devices, e.g., viaaccess requests, queries or other data retrieval protocols, for avariety of operations with respect to the information stored by themedium. As such, for example, a computer-readable medium can compriseexecutable instructions stored thereon that, in response to execution,can cause a system comprising a processor to perform operations,comprising initiating a slice event based on a slice event instructioncorresponding to analysis of network analytics for various portions of anetwork, and can be based on rules and/or inferences related to theanalysis of the network analytics.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and comprises any informationdelivery or transport media. The term “modulated data signal” or signalsrefers to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in one or moresignals. By way of example, and not limitation, communication mediacomprise wired media, such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media.

It can be noted that FIG. 10 describes software that acts as anintermediary between users and computer resources described in suitableoperating environment 1000. Such software comprises an operating system1028. Operating system 1028, which can be stored on disk storage 1024,acts to control and allocate resources of computer system 1012. Systemapplications 1030 take advantage of the management of resources byoperating system 1028 through program modules 1032 and program data 1034stored either in system memory 1016 or on disk storage 1024. It is to benoted that the disclosed subject matter can be implemented with variousoperating systems or combinations of operating systems.

A user can enter commands or information into computer 1012 throughinput device(s) 1036. In some embodiments, a user interface can allowentry of user preference information, etc., and can be embodied in atouch sensitive display panel, a mouse/pointer input to a graphical userinterface (GUI), a command line controlled interface, etc., allowing auser to interact with computer 1012. Input devices 1036 comprise, butare not limited to, a pointing device such as a mouse, trackball,stylus, touch pad, keyboard, microphone, joystick, game pad, satellitedish, scanner, TV tuner card, digital camera, digital video camera, webcamera, cell phone, smartphone, tablet computer, etc. These and otherinput devices connect to processing unit 1014 through system bus 1018 byway of interface port(s) 1038. Interface port(s) 1038 comprise, forexample, a serial port, a parallel port, a game port, a universal serialbus, an infrared port, a Bluetooth port, an IP port, or a logical portassociated with a wireless service, etc. Output device(s) 1040 use someof the same type of ports as input device(s) 1036.

Thus, for example, a universal serial busport can be used to provideinput to computer 1012 and to output information from computer 1012 toan output device 1040. Output adapter 1042 is provided to illustratethat there are some output devices 1040 like monitors, speakers, andprinters, among other output devices 1040, which use special adapters.Output adapters 1042 comprise, by way of illustration and notlimitation, video and sound cards that provide means of connectionbetween output device 1040 and system bus 1018. It should be noted thatother devices and/or systems of devices provide both input and outputcapabilities such as remote computer(s) 1044.

Computer 1012 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1044. Remote computer(s) 1044 can be a personal computer, a server, arouter, a network PC, cloud storage, a cloud service, code executing ina cloud-computing environment, a workstation, a microprocessor-basedappliance, a peer device, or other common network node and the like, andtypically comprises many or all of the elements described relative tocomputer 1012. A cloud computing environment, the cloud, or othersimilar terms can refer to computing that can share processing resourcesand data to one or more computer and/or other device(s) on an as neededbasis to enable access to a shared pool of configurable computingresources that can be provisioned and released readily. Cloud computingand storage solutions can store and/or process data in third-party datacenters which can leverage an economy of scale and can view accessingcomputing resources via a cloud service in a manner similar to asubscribing to an electric utility to access electrical energy, atelephone utility to access telephonic services, etc.

For purposes of brevity, only a memory storage device 1046 isillustrated with remote computer(s) 1044. Remote computer(s) 1044 islogically connected to computer 1012 through a network interface 1048and then physically connected by way of communication connection 1050.Network interface 1048 encompasses wire and/or wireless communicationnetworks such as local area networks and wide area networks. Local areanetwork technologies comprise fiber distributed data interface, copperdistributed data interface, Ethernet, Token Ring and the like. Wide areanetwork technologies comprise, but are not limited to, point-to-pointlinks, circuit-switching networks like integrated services digitalnetworks and variations thereon, packet switching networks, and digitalsubscriber lines. As noted below, wireless technologies may be used inaddition to or in place of the foregoing.

Communication connection(s) 1050 refer(s) to hardware/software employedto connect network interface 1048 to bus 1018. While communicationconnection 1050 is shown for illustrative clarity inside computer 1012,it can also be external to computer 1012. The hardware/software forconnection to network interface 1048 can comprise, for example, internaland external technologies such as modems, comprising regular telephonegrade modems, cable modems and digital subscriber line modems,integrated services digital network adapters, and Ethernet cards.

The above description of illustrated embodiments of the subjectdisclosure, comprising what is described in the Abstract, is notintended to be exhaustive or to limit the disclosed embodiments to theprecise forms disclosed. While specific embodiments and examples aredescribed herein for illustrative purposes, various modifications arepossible that are considered within the scope of such embodiments andexamples, as those skilled in the relevant art can recognize.

In this regard, while the disclosed subject matter has been described inconnection with various embodiments and corresponding Figures, whereapplicable, it is to be understood that other similar embodiments can beused or modifications and additions can be made to the describedembodiments for performing the same, similar, alternative, or substitutefunction of the disclosed subject matter without deviating therefrom.Therefore, the disclosed subject matter should not be limited to anysingle embodiment described herein, but rather should be construed inbreadth and scope in accordance with the appended claims below.

As it employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to comprising, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit, a digital signalprocessor, a field programmable gate array, a programmable logiccontroller, a complex programmable logic device, a discrete gate ortransistor logic, discrete hardware components, or any combinationthereof designed to perform the functions described herein. Processorscan exploit nano-scale architectures such as, but not limited to,molecular and quantum-dot based transistors, switches and gates, inorder to optimize space usage or enhance performance of user equipment.A processor may also be implemented as a combination of computingprocessing units.

As used in this application, the terms “component,” “system,”“platform,” “layer,” “selector,” “interface,” and the like are intendedto refer to a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution. As an example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a program,and/or a computer. By way of illustration and not limitation, both anapplication running on a server and the server can be a component. Oneor more components may reside within a process and/or thread ofexecution and a component may be localized on one computer and/ordistributed between two or more computers. In addition, these componentscan execute from various computer readable media having various datastructures stored thereon. The components may communicate via localand/or remote processes such as in accordance with a signal having oneor more data packets (e.g., data from one component interacting withanother component in a local system, distributed system, and/or across anetwork such as the Internet with other systems via the signal). Asanother example, a component can be an apparatus with specificfunctionality provided by mechanical parts operated by electric orelectronic circuitry, which is operated by a software or a firmwareapplication executed by a processor, wherein the processor can beinternal or external to the apparatus and executes at least a part ofthe software or firmware application. As yet another example, acomponent can be an apparatus that provides specific functionalitythrough electronic components without mechanical parts, the electroniccomponents can comprise a processor therein to execute software orfirmware that confers at least in part the functionality of theelectronic components.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form. Moreover, the use of any particularembodiment or example in the present disclosure should not be treated asexclusive of any other particular embodiment or example, unlessexpressly indicated as such, e.g., a first embodiment that has aspect Aand a second embodiment that has aspect B does not preclude a thirdembodiment that has aspect A and aspect B. The use of granular examplesand embodiments is intended to simplify understanding of certainfeatures, aspects, etc., of the disclosed subject matter and is notintended to limit the disclosure to said granular instances of thedisclosed subject matter or to illustrate that combinations ofembodiments of the disclosed subject matter were not contemplated at thetime of actual or constructive reduction to practice.

Further, the term “include” is intended to be employed as an open orinclusive term, rather than a closed or exclusive term. The term“include” can be substituted with the term “comprising” and is to betreated with similar scope, unless otherwise explicitly used otherwise.As an example, “a basket of fruit including an apple” is to be treatedwith the same breadth of scope as, “a basket of fruit comprising anapple.”

Moreover, terms like “user equipment (UE),” “mobile station,” “mobile,”subscriber station,” “subscriber equipment,” “access terminal,”“terminal,” “handset,” and similar terminology, refer to a wirelessdevice utilized by a subscriber or user of a wireless communicationservice to receive or convey data, control, voice, video, sound, gaming,or substantially any data-stream or signaling-stream. The foregoingterms are utilized interchangeably in the subject specification andrelated drawings. Likewise, the terms “access point,” “base station,”“Node B,” “evolved Node B,” “eNodeB,” “home Node B,” “home accesspoint,” and the like, are utilized interchangeably in the subjectapplication, and refer to a wireless network component or appliance thatserves and receives data, control, voice, video, sound, gaming, orsubstantially any data-stream or signaling-stream to and from a set ofsubscriber stations or provider enabled devices. Data and signalingstreams can comprise packetized or frame-based flows. Data or signalinformation exchange can comprise technology, such as, single user (SU)multiple-input and multiple-output (MIMO) (SU MIMO) radio(s), multipleuser (MU) MIMO (MU MIMO) radio(s), long-term evolution (LTE), LTEtime-division duplexing (TDD), global system for mobile communications(GSM), GSM EDGE Radio Access Network (GERAN), Wi Fi, WLAN, WiMax,CDMA2000, LTE new radio-access technology (LTE-NX), massive MIMOsystems, etc.

Additionally, the terms “core-network”, “core”, “core carrier network”,“carrier-side”, or similar terms can refer to components of atelecommunications network that typically provides some or all ofaggregation, authentication, call control and switching, charging,service invocation, or gateways. Aggregation can refer to the highestlevel of aggregation in a service provider network wherein the nextlevel in the hierarchy under the core nodes is the distribution networksand then the edge networks. UEs do not normally connect directly to thecore networks of a large service provider but can be routed to the coreby way of a switch or radio access network. Authentication can refer toauthenticating a user-identity to a user-account. Authentication can, insome embodiments, refer to determining whether a user-identityrequesting a service from a telecom network is authorized to do sowithin the network or not. Call control and switching can referdeterminations related to the future course of a call stream acrosscarrier equipment based on the call signal processing. Charging can berelated to the collation and processing of charging data generated byvarious network nodes. Two common types of charging mechanisms found inpresent day networks can be prepaid charging and postpaid charging.Service invocation can occur based on some explicit action (e.g. calltransfer) or implicitly (e.g., call waiting). It is to be noted thatservice “execution” may or may not be a core network functionality asthird party network/nodes may take part in actual service execution. Agateway can be present in the core network to access other networks.Gateway functionality can be dependent on the type of the interface withanother network.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer,”“prosumer,” “agent,” and the like are employed interchangeablythroughout the subject specification, unless context warrants particulardistinction(s) among the terms. It should be appreciated that such termscan refer to human entities, machine learning components, or automatedcomponents (e.g., supported through artificial intelligence, as througha capacity to make inferences based on complex mathematical formalisms),that can provide simulated vision, sound recognition and so forth.

Aspects, features, or advantages of the subject matter can be exploitedin substantially any, or any, wired, broadcast, wirelesstelecommunication, radio technology or network, or combinations thereof.Non-limiting examples of such technologies or networks comprisebroadcast technologies (e.g., sub-Hertz, extremely low frequency, verylow frequency, low frequency, medium frequency, high frequency, veryhigh frequency, ultra-high frequency, super-high frequency, extremelyhigh frequency, terahertz broadcasts, etc.); Ethernet; X.25;powerline-type networking, e.g., Powerline audio video Ethernet, etc.;femtocell technology; Wi-Fi; worldwide interoperability for microwaveaccess; enhanced general packet radio service; second generationpartnership project (2G or 2GPP); third generation partnership project(3G or 3GPP); fourth generation partnership project (4G or 4GPP); longterm evolution (LTE); fifth generation partnership project (5G or 5GPP);third generation partnership project universal mobile telecommunicationssystem; third generation partnership project 2; ultra mobile broadband;high speed packet access; high speed downlink packet access; high speeduplink packet access; enhanced data rates for global system for mobilecommunication evolution radio access network; universal mobiletelecommunications system terrestrial radio access network; or long termevolution advanced. As an example, a millimeter wave broadcasttechnology can employ electromagnetic waves in the frequency spectrumfrom about 30 GHz to about 300 GHz. These millimeter waves can begenerally situated between microwaves (from about 1 GHz to about 30 GHz)and infrared (IR) waves, and are sometimes referred to extremely highfrequency (EHF). The wavelength (λ) for millimeter waves is typically inthe 1-mm to 10-mm range.

The term “infer” or “inference” can generally refer to the process ofreasoning about, or inferring states of, the system, environment, user,and/or intent from a set of observations as captured via events and/ordata. Captured data and events can include user data, device data,environment data, data from sensors, sensor data, application data,implicit data, explicit data, etc. Inference, for example, can beemployed to identify a specific context or action, or can generate aprobability distribution over states of interest based on aconsideration of data and events. Inference can also refer to techniquesemployed for composing higher-level events from a set of events and/ordata. Such inference results in the construction of new events oractions from a set of observed events and/or stored event data, whetherthe events, in some instances, can be correlated in close temporalproximity, and whether the events and data come from one or severalevent and data sources. Various classification schemes and/or systems(e.g., support vector machines, neural networks, expert systems,Bayesian belief networks, fuzzy logic, and data fusion engines) can beemployed in connection with performing automatic and/or inferred actionin connection with the disclosed subject matter.

What has been described above includes examples of systems and methodsillustrative of the disclosed subject matter. It is, of course, notpossible to describe every combination of components or methods herein.One of ordinary skill in the art may recognize that many furthercombinations and permutations of the claimed subject matter arepossible. Furthermore, to the extent that the terms “includes,” “has,”“possesses,” and the like are used in the detailed description, claims,appendices and drawings such terms are intended to be inclusive in amanner similar to the term “comprising” as “comprising” is interpretedwhen employed as a transitional word in a claim.

What is claimed is:
 1. A device, comprising: a processor; and a memorythat stores executable instructions that, when executed by theprocessor, facilitate performance of operations, comprising: receivingfirst network state information corresponding to a first state of aportion of a network, wherein the portion of the network comprisesnetwork devices enabling first communication between a first userequipment and a first device via the network devices; determining afirst network slice of possible network slices implementable by thenetwork devices to facilitate the first communication between the firstuser equipment and the first device via the network, wherein thedetermining the first network slice is based on the first network stateinformation; selecting the first network slice from the possible networkslices based on the first network slice satisfying a performance rulerelated to a defined performance of the first network slice; and inresponse to the selecting the first network slice, initiating deploymentof the first network slice to enable the first communication between thefirst user equipment and the first device via the first network slice.2. The device of claim 1, wherein the selecting the first network slicefrom among the possible network slices is further based on an orderingof the possible network slices, and wherein the ordering of the possiblenetwork slices relates to relative performances of the possible networkslices.
 3. The device of claim 2, wherein the ordering of the possiblenetwork slices is a rank ordering of the possible network slices basedon ranks representative of the relative performances.
 4. The device ofclaim 1, wherein the initiating the deployment of the first networkslice comprises initiating an adaptation to a parameter of a secondnetwork slice that already facilitates the first communication betweenthe first user equipment and the first device.
 5. The device of claim 1,wherein the initiating the deployment of the first network slicecomprises generating the first network slice and causing the firstcommunication between the first user equipment and the first device tooccur via the first network slice.
 6. The device of claim 5, wherein thecausing the first communication between the first user equipment and thefirst device to occur via the first network slice comprises causing anexisting communication between the first user equipment and the firstdevice via a second network slice to cease.
 7. The device of claim 5,wherein the causing the first communication between the first userequipment and the first device to occur via the first network slicecomprises causing an existing communication between the first userequipment and the first device via a second network slice to continue.8. The device of claim 1, wherein the defined performance of the firstnetwork slice is a first defined performance, wherein the deployment ofthe first network slice is a first deployment, and wherein theoperations further comprise: receiving second network state informationcorresponding to a second state of the portion of the network comprisingthe network devices enabling the first communication between the firstuser equipment and the first device via the network; selecting a secondnetwork slice from the possible network slices based on the secondnetwork slice satisfying the performance rule related to a seconddefined performance of the second network slice; and in response to theselecting the second network slice, initiating a second deployment ofthe second network slice to enable a second communication between asecond user equipment and a second device to occur via the secondnetwork slice.
 9. The device of claim 1, wherein the deployment of thefirst network slice is a first deployment, and wherein the operationsfurther comprise: in response to the selecting the first network slice,initiating a second deployment of the first network slice to enable asecond communication between a second user equipment and a second devicevia the first network slice.
 10. The device of claim 9, wherein thefirst device is a different device than the second device.
 11. Thedevice of claim 1, wherein the determining the first network slicecomprises determining a first performance metric based on an inferenceformed from trending network infrastructure use data comprised in thefirst network state information.
 12. The device of claim 1, wherein thedetermining the first network slice comprises determining a firstperformance metric based on an inference formed from available servicesdata comprised in the first network state information.
 13. The device ofclaim 1, wherein the determining the first network slice comprisesdetermining a first performance metric based on an inference formed fromhistorical network traffic demand data comprised in the first networkstate information.
 14. The device of claim 1, wherein the initiating thedeployment of the first network slice comprises triggering an opennetwork automation platform engine to provide a design template for thefirst network slice.
 15. A method, comprising: determining, by a systemcomprising a processor and a memory, a network slice event instructioncorresponding to a possible network slice of possible network slicesimplementable by network devices of a wireless network to facilitatecommunication between a user equipment and a device via a portion of thewireless network, wherein the determining the network slice eventinstruction is based on an analysis of analytic data corresponding to atleast a portion of the wireless network; and initiating, by the system,a network slice event based on the network slice event instruction,wherein the network slice event results in modification of a group ofnetwork slices of the wireless network.
 16. The method of claim 15,wherein the initiating the network slice event results in themodification of the group of network slices comprising adding a newnetwork slice to the group of network slices of the wireless network.17. The method of claim 15, wherein the initiating the network sliceevent results in the modification of the group of network slicescomprising removing an existing network slice from the group of networkslices of the wireless network.
 18. The method of claim 15, wherein thedetermining the network slice event instruction is based on an inferencerelated to the analytic data corresponding to at least a first portionof the wireless network.
 19. A machine-readable storage medium,comprising executable instructions that, when executed by a processor,facilitate performance of operations, comprising: determining a networkslice event instruction corresponding to a possible network slice ofpossible network slices implementable by network devices of a network tofacilitate communication between a user equipment and a device via aportion of the network, wherein the determining the network slice eventinstruction is based on a first analysis of first analytic datacorresponding to a first portion of the network; and facilitatingprogression of a network slice event based on the network slice eventinstruction, wherein the network slice event results in modification ofa group of network slices of the network.
 20. The machine-readablestorage medium of claim 19, wherein the first portion of the network isa core-network portion of the network comprising core-network devices,wherein the determining the network slice event instruction is furtherbased on a second analysis of second analytic data corresponding to asecond portion of the network, and wherein the second portion of thenetwork is other than the core-networks portion of the network.